ESM 244 Lab 1

ESM 244 Lab 1:

Objectives:

• ESM 206 basics review (github > project > working directories > rmarkdown > read > wrangle > visualize > github)
• Intro to sf (simple features)
• Intro to Shiny apps (ui + server, Old Faithful example)

Packages:

• tidyverse
• sf

library(tidyverse)
library(sf)
library(leaflet)
library(tmap)

1. Review: read, explore, wrangle, visualize

Pinot noir reviews scraped from Wine Enthusiast (scraped, compiled and cleaned for Kaggle by user zackthoutt at https://www.kaggle.com/zynicide/wine-reviews).

1. Read in the data

wine <- read_csv("wine_data.csv")

2. Basic wrangling with piping (%>%) refresher

wine_new <- wine %>% 
  select(country, province, winery, region_1, points, price) %>% 
  rename(state = province) %>% 
  filter(state == "California" | state == "Oregon" | state == "Washington") %>% 
  mutate(ppd = round(points/price,2)) %>% # points per dollar!
  arrange(-ppd) 

3. Summary stats by group using group_by and summarize()

wine_summary <- wine_new %>% 
  group_by(state) %>% 
  summarize(
    mean_ppp = mean(ppd, na.rm = TRUE),
    median_ppp = median(ppd, na.rm = TRUE),
    min = min(ppd, na.rm = TRUE),
    count = length(state)
  )

wine_summary

## # A tibble: 3 x 5
##   state      mean_ppp median_ppp   min count
##   <chr>         <dbl>      <dbl> <dbl> <int>
## 1 California     2.43       2.02  0.63  6896
## 2 Oregon         2.42       2.12  0.34  2786
## 3 Washington     3.35       2.84  1.4     34

4. A few gg graphics

wine_hists <- ggplot(wine_new, aes(x = ppd)) +
  geom_histogram(aes(fill = state), alpha = 0.7) +
  scale_fill_manual(values = c("royalblue", "mediumorchid1","orange")) +
  facet_wrap(~state, scales = "free")

wine_hists

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

wine_points <- ggplot(wine_new, aes(x = price, y = points)) +
  geom_point(aes(color = state), alpha = 0.1) +
  geom_smooth(color = "black", method = "lm", size = 0.4, se = FALSE) +
  scale_color_manual(values = c("royalblue","mediumorchid1","orange")) +
  theme_light() +
  labs(x = "Price", y = "Points") +
  theme(legend.position = "NA") +
  facet_wrap(~state)

wine_points

2. California counties (spatial data)

Data: CA.gov California Open Data Portal: https://data.ca.gov/dataset/ca-geographic-boundaries

File types (see more at https://www.census.gov/geo/maps-data/data/tiger-line.html):

• Feature geometry: .shp
• Index of feature geometry: .shx
• Character encoding: .cpg
• Attribute information: .dbf
• Coodinate system information: .prj
• International Organization for Standardization (ISO 191) metadata: .shp.iso.xml
• ISO 191 (entity and attribute) metatdata: .shp.ea.iso.xml

Attribute variables we’ll use:

1. Use st_read() in the sf package to read in shape file data. You’ll need to include the directory (just “.” if in the project working directory) and the file prefix argument ‘layer = “prefix_here”’.

ca_counties <- st_read(dsn = ".", layer = "CA_Counties_TIGER2016")

A really cool thing about the sf package is that geometries are sticky - that means that we basically get to work with spatial attributes like a normal tibble/data frame, but the geometries (spatial information) stick to it.

2. Select only the ALAND attribute + County name. Notice that geometries stick. Use plot() to see counties.

ca_land <- ca_counties %>% 
  select(NAME, ALAND)

# plot(ca_land)

3. Join the spatial data to income data (ca_pop_inc.csv)

# Read pop/income data, then make sure county names column matches
ca_pop_inc <- read_csv("ca_pop_inc.csv") %>% 
  rename(NAME = COUNTY)

# Join the two: 
ca_df <- full_join(ca_land, ca_pop_inc) %>% 
  select(NAME, MedFamilyIncome)

4. Use geom_sf to create a map using ggplot

# Make a map: 
ca_income <- ggplot(ca_df) +
  geom_sf(aes(fill = MedFamilyIncome), color = "white", size = 0.2) +
  scale_fill_gradientn(colors = c("blue","mediumorchid1","orange")) +
  theme_minimal()

ca_income

5. …or using leaflet

# Show that this doesn't work:

#leaflet(ca_df) %>% 
#  addPolygons() 

# Oh no, the projection is wrong! We need it to match the projection that leaflet uses (WGS84)

ca_df_transform <- st_transform(ca_df, crs = 4326)

# Now try that again...

leaflet(ca_df_transform) %>% 
  addTiles() %>% # Adds bg
  addPolygons(weight = 1.0,
              opacity = 1.0,
              color = "white",
              fillOpacity = 0.5,
              fillColor = ~colorQuantile("YlOrRd", MedFamilyIncome)(MedFamilyIncome)
              ) # Adds polygons

6. …or using tmap!

tmap_mode("view")

## tmap mode set to interactive viewing

## tmap mode set to interactive viewing
# if (Sys.getenv("USER") != "CRAN")

tm_shape(ca_df_transform) + tm_fill("MedFamilyIncome", alpha = 0.5)